Dataset Information

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

ABSTRACT:

Background

Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.

Methods

We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression.

Results

Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027-91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576-8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824.

Conclusions

Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

SUBMITTER: Sun BY

PROVIDER: S-EPMC9178852 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

Sun Bao-Ye BY Gu Pei-Yi PY Guan Ruo-Yu RY Zhou Cheng C Lu Jian-Wei JW Yang Zhang-Fu ZF Pan Chao C Zhou Pei-Yun PY Zhu Ya-Ping YP Li Jia-Rui JR Wang Zhu-Tao ZT Gao Shan-Shan SS Gan Wei W Yi Yong Y Luo Ye Y Qiu Shuang-Jian SJ

World journal of surgical oncology 20220608 1

<h4>Background</h4>Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.<h4>Methods</h4>We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MR ...[more]

PMID: 35676669

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Project description:BackgroundHepatocellular carcinoma (HCC) represents a considerable burden to patients and health systems. Microvascular invasion (MVI) is a significant risk factor for HCC recurrence and survival after hepatectomy. We aimed to establish a preoperative MVI prediction model based on readily available clinical and radiographic characteristics using machine learning algorithms.MethodsTwo independent cohorts of patients with HCC who underwent hepatectomy were included in the analysis and divided into a derivation set (466 patients), an internal validation set (182 patients), and an external validation set (140 patients). Least absolute shrinkage and selection operator (LASSO) analysis was used to optimize variable selection. We constructed the MVI prediction model using several machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest, extreme gradient boosting, and neural network. Performance of the model was assessed in terms of discrimination, calibration, and clinical usefulness.ResultsThe three most significant variables associated with MVI-α-fetoprotein, protein induced by vitamin K absence or antagonist-II, and tumor size-were identified by the LASSO analysis. Among the machine learning algorithms, the logistic regression model achieved the largest area under the receiver operating characteristic curve and was presented in the form of a user-friendly, online calculator. The concordance (C)-statistic of the model was 0.745 [95% confidence interval (CI): 0.701-0.790] for the derivation set, 0.771 (95% CI: 0.703-0.839) for the internal validation set, and 0.812 (95% CI: 0.734-0.891) for the external validation set. The Hosmer-Lemeshow calibration test and calibration plot indicated a good fit for all 3 data sets. Decision curve analysis showed the model was clinically useful.ConclusionsThis study provided a convenient and explainable approach for MVI prediction before surgical intervention. Our model may assist clinicians in determining the optimal therapeutic modality and facilitate precision medicine for HCC.

Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Project description:ObjectiveThe aim of this study was to explore the relation between preoperative tumor size based on magnetic resonance imaging (MRI) and the surgical pathologic staging parameters (deep myometrial invasion, cervical stroma invasion, and metastatic lymph nodes) and to assess the prognostic impact of tumor size in endometrial carcinomas. Interobserver variability for the different tumor size measurements was also assessed.Methods/materialsPreoperative pelvic MRI of 212 patients with histologically confirmed endometrial carcinomas was read independently by 3 radiologists. Maximum tumor diameters were measured in 3 orthogonal planes (anteroposterior, transverse, and craniocaudal planes [CC]), and tumor volumes were estimated. Tumor size was analyzed in relation to surgical staging results and patient survival. The multivariate analyses were adjusted for preoperative risk status based on endometrial biopsy. Intraclass correlation coefficients and receiver operating characteristics curves for the different tumor measurements were also calculated.ResultsAnteroposterior tumor diameter independently predicted deep myometrial invasion (P < 0.001), whereas CC tumor diameter tended to independently predict lymph node metastases (P = 0.06). Based on receiver operating characteristic curves, the following tumor size cutoff values were identified: anteroposterior diameter greater than 2 cm predicted deep myometrial invasion (unadjusted odds ratio [OR], 12.4; P < 0.001; adjusted OR, 6.7; P < 0.001) and CC diameter greater than 4 cm predicted lymph node metastases (unadjusted OR, 6.2; P < 0.001; adjusted OR, 4.9; P = 0.009). Large tumor size was associated with reduced progression/recurrence-free survival (P ≤ 0.005 for all size parameters), and CC diameter had an independent impact on survival (adjusted hazards ratio, 1.04; P = 0.009). The interobserver variability for the different size measurements was very low (intraclass correlation coefficient, 0.78-0.85).ConclusionsAnteroposterior tumor diameter greater than 2 cm predicts deep myometrial invasion, and CC tumor diameter greater than 4 cm predicts lymph node metastases. Tumor size is a strong prognostic factor in endometrial carcinomas. Preoperative tumor measurements based on MRI may potentially improve preoperative risk stratification models and thus enable better tailored surgical treatment in endometrial cancer.

Project description:ObjectivesTo develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm.MethodsBetween March 2012 and September 2019, 356 patients with pathologically confirmed solitary HCC ≤ 5 cm who underwent preoperative gadoxetate disodium-enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number and distribution of invaded vessels. Radiomics features were extracted from DWI, arterial, portal venous, and hepatobiliary phase images in regions of the entire tumor, peritumoral area ≤ 10 mm, and randomly selected liver tissue. Multivariate analysis identified the independent predictors for MVI and RFS, with nomogram visualized the ultimately predictive models.ResultsElevated alpha-fetoprotein, total bilirubin and radiomics values, peritumoral enhancement, and incomplete or absent capsule enhancement were independent risk factors for MVI. The AUCs of MVI nomogram reached 0.920 (95% CI: 0.861-0.979) using random forest and 0.879 (95% CI: 0.820-0.938) using logistic regression analysis in validation cohort (n = 106). With the 5-year RFS rate of 68.4%, the median RFS of MVI-positive (M2 and M1) and MVI-negative (M0) patients were 30.5 (11.9 and 40.9) and > 96.9 months (p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, and alanine aminotransferase independently predicted recurrence, yielding AUC of 0.654 (95% CI: 0.538-0.769, n = 99) in RFS validation cohort. Instead of histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest achieved comparable accuracy in MVI stratification and RFS prediction.ConclusionsPreoperative radiomics-based nomogram using random forest is a potential biomarker of MVI and RFS prediction for solitary HCC ≤ 5 cm.Key points• The radiomics score was the predominant independent predictor of MVI which was the primary independent risk factor for postoperative recurrence. • The radiomics-based nomogram using either random forest or logistic regression analysis has obtained the best preoperative prediction of MVI in HCC patients so far. • As an excellent substitute for the invasive histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest (MVI-RF) achieved comparable accuracy in MVI stratification and outcome, reinforcing the radiologic understanding of HCC angioinvasion and progression.

Dataset Information

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

Similar Datasets

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